Applications of Neural Networks in Hadron Physics

The Bayesian approach for the feed-forward neural networks is reviewed. Its potential for usage in hadron physics is discussed. As an example of the application, the study of the two-photon exchange effect is presented. We focus on the model comparison, the estimation of the systematic uncertainties due to the choice of the model and the over-fitting. As an illustration, the predictions of the cross sections ratio d sigma(e(+)p -> e(+)p)/d sigma(e(-)p -> e(-)p) are given together with the estimate of the uncertainty due to the parametrization choice.